1. Field of the Invention
The present invention is directed to an inertial navigation system for mobile objects such as vehicles and the like. In particular, the present invention is directed to such an inertial navigation system for mobile objects having constraints.
2. Description of Related Art
There is an increasing need for smaller and more accurate Inertial Navigation Systems (hereinafter xe2x80x9cINSxe2x80x9d) for navigation of mobile objects. Such INS applications include vehicle navigation, and positioning, guidance, or navigation of objects such as mobile probes and the like. In vehicle navigation applications, such INSs may be used for navigation of automobiles, railcars including locomotives, etc. The implementation of INSs in many applications is facilitated by the availability of Global Positioning System (hereinafter xe2x80x9cGPSxe2x80x9d) receivers or other positioning systems.
For instance, U.S. Pat. No. 6,081,230 to Hoshino et al. (hereinafter xe2x80x9cHoshino et al.xe2x80x9d) discloses a navigation system that can enhance the position determining accuracy of a mobile object without utilizing high precision measuring instruments. The disclosed navigation system of Hoshino et al. includes a GPS range measuring device, an angular velocity measuring device, a velocity measuring device and an azimuth measuring device, which are used to measure the motion of the mobile object. The navigation system disclosed also includes a GPS range error estimating device, an angular velocity error estimating device, a velocity error estimating device, and an azimuth error estimating device, all of which are used to estimate errors associated with the corresponding measuring devices.
In accordance with Hoshino et al., a position calculating device calculates the position of the mobile object based on the outputs of the error estimating devices. The error estimating devices are implemented using Kalman filters and averaging processes, the factors of the errors of the individual measuring devices being assumed. The outputs of the various measuring devices are thereby corrected so that the position of the mobile object can always be determined at a relatively high level of accuracy without employing a high precision sensor. However, due to the limitations and imprecise nature of the devices, the accuracy of inertial navigational systems such as those described in Hoshino et al. is limited, especially in those instances where GPS position data is not readily available, for example, when the GPS signal to the inertial navigational system is obstructed.
In addition, although the cost of INSs has been decreasing due to various inventions such as the ring laser gyro and the fiber optic gyro, commercial INSs still cost tens of thousands of dollars. Recent technology advances have produced sensors that are smaller and less expensive which can be readily implemented in INSs. One such development is the introduction of MicroElectroMechanical Systems (hereinafter xe2x80x9cMEMSxe2x80x9d), which are miniature mechanical devices that are manufactured using techniques similar to those used in the production of integrated circuits. However, at their present stage of development, MEMS are less accurate than other more costly inertial sensors for use in INSs.
Therefore, there still exists an unfulfilled need for INSs that avoids the limitations of the prior art systems. In particular, there still exists an unfulfilled need for low cost INSs that provides accuracy at levels similar to that of currently available, expensive, commercial INSs. In addition, there still exists an unfulfilled need for such INSs that can provide accurate navigational information even when GPS position data is occasionally or intermittently unavailable.
In view of the forgoing, one advantage of the present invention is in providing an Aided Inertial Navigation System (hereinafter xe2x80x9cAINSxe2x80x9d) that improves the accuracy of navigation for any type of inertial sensors.
Another advantage of the present invention is in providing such an AINS that is more affordable than presently available commercial INSs while providing similar levels of accuracy.
Yet another advantage of the present invention is in providing such an AINS that can provide accurate navigational information even when GPS position data is occasionally or intermittently unavailable.
A significant advantage of one embodiment of the present invention is that it allows use of various types of inertial sensors, including MEMS sensors. Although individual MEMS or other types of sensors may not have the accuracy needed for use in high-precision commercial INSs, the accuracy is increased by the AINSs in accordance with the present invention by aiding the AINSs with auxiliary input data from additional sources, and by making use of information from the known constraints on a mobile object""s motion, if such constraints exist. Thus, by allowing the use of various, cost effective types of inertial sensors, such as MEMS, the AINS in accordance with the present invention can be implemented economically.
These and other advantages are attained by an aided inertial navigation system (AINS) in accordance with one embodiment of the present invention for navigating a mobile object having constraints which constrain mobility of the mobile object to a path. The AINS comprises an inertial measurement unit, a processor, and an error correction device. The inertial measurement unit is adapted to provide acceleration data and angular velocity data of the mobile object, the mobile object having constraints which constrain mobility of the mobile object to a path. The processor is adapted to receive the acceleration data and angular velocity data from the inertial measurement unit, and to provide output data with position output indicative of position of the mobile object. The error correction device is adapted to receive as input, state and dynamics information of the mobile object, and provide as output, state corrections to the processor. In accordance with one embodiment of the present invention, the processor enhances the position output based on the state corrections and the constraints to the mobile object to increase accuracy of the position output.
In one embodiment, the inertial measurement unit may include an accelerometer that provides the acceleration data, and/or a gyroscope that provides the angular velocity data. The processor may be an inertial navigation and sensor compensation unit. The output data may further include a velocity output indicative of speed of the mobile object, and an attitude output indicative of direction of movement of the mobile object. Moreover, the output data may also include an accuracy output indicative of accuracy of the position output which may be expressed as a confidence interval for distance along the path, a confidence circle, and/or a confidence ellipse.
In accordance with another embodiment, the error correction device is further adapted to receive auxiliary input data including positional input data, map information associated with the path, speed data, wheel-angle data, and/or discrete data. The state corrections provided to the processor may then be based on the auxiliary input data as well as the state and dynamics information. In this regard, the error correction device may be implemented as a Kalman filter that is further adapted to receive the auxiliary input data. The positional input data may be provided by a Global Positioning System, a Differential GPS, an ultrasonic positioning system, and/or a radio-frequency positioning system. The speed data may be provided by an odometer, a wheel tachometer, and/or a Doppler radar. The wheel angle data may be provided by a wheel angle sensor and/or a truck angle sensor. The discrete data may be provided by a transponder and a rail detector. In such an embodiment, the Kalman filter may be provided with zero-azimuth-change observations when the mobile object is stationary. The map information may include coordinates of a series of map points marking at least one map segment and/or include along-path distances between the series of map points. The processor may be further adapted to calculate a maximum distance error between a segment of the path and the map segment.
The AINS in accordance with the present invention may be readily applied to mobile objects that are constrained to move along a path. For example, the mobile object may be an automobile where the path is defined by a road, a railcar or a trolley where the path is defined by a track, or a mobile probe where the path is defined by a pipe.
In accordance with another embodiment of the present invention, the AINS includes an inertial measurement unit adapted to provide acceleration data and/or angular velocity data of the mobile object, a processor adapted to receive the acceleration data and/or angular velocity data from the inertial measurement unit, and to provide output data with position output indicative of position of the mobile object, and a Kalman filter adapted to receive as input, state and dynamics information of the mobile object, and provide as output, state corrections to the processor, the Kalman filter being provided with zero-azimuth-change observations when the mobile object is stationary, where the processor enhances the position output based on the state corrections to increase accuracy of the position output.
In accordance with another aspect of the present invention, a method for navigating a mobile object is provided comprising the steps of constraining mobility of the mobile object to a path, monitoring acceleration and angular velocity of the mobile object, providing acceleration data and angular velocity data, determining output data with position output indicative of position of the mobile object based on the acceleration data and angular velocity data, and enhancing accuracy of the position output indicative of position of the mobile object based on the constraints to mobility of the mobile object.
The method of the present invention may include the step of providing map information associated with the path, wherein the step of enhancing accuracy of the position output is also based on the map information. In addition, or in the alternative, the step of enhancing accuracy of the position output may be based on state and dynamics information of the mobile object, the state and dynamics information being derived from the acceleration data and angular velocity data.
The method may further include the steps of determining velocity and attitude of the mobile object, and providing a velocity output indicative of speed of the mobile object and an attitude output indicative of direction of the mobile object. The method may also include the step of determining accuracy of the position output, the output data further including an accuracy output indicative of accuracy of the position output.
The step of enhancing accuracy of the position output may also include the step of generating state corrections using a Kalman filter based on positional input data, speed data, map information, wheel angle data, and discrete data. In this regard, the method may further include the step of determining smoothly varying distance along path for use as the speed data. The map information preferably includes coordinates of a series of map points marking at least one map segment, and along-path distances between the series of points. The method may also include the steps of determining where position of the mobile object coincides with a known map point, and further enhancing accuracy of the position output based on the known map point. In addition, or alternatively, the method may include the steps of monitoring along path distance, and comparing the along path distance to length of the map segment to determine accuracy of the map information. In addition, a maximum distance error between a segment of the path and the map segment may be calculated. When a Kalman filter is used, the Kalman filter may be provided with zero-azimuth-change observations when the mobile object is stationary.
In accordance with another aspect of the present invention, a method for navigating a mobile object is provided comprising the steps of monitoring acceleration and angular velocity of the mobile object, providing acceleration data and angular velocity data, determining output data with position output indicative of position of the mobile object for navigating the mobile object based on the acceleration data and angular velocity data, providing a Kalman filter adapted to receive state and dynamics information of the mobile object as input, and providing state corrections as outputs, and providing the Kalman filter with zero-azimuth-change observations when the mobile object is stationary to enhance accuracy of the position output indicative of position of the mobile object.
In another embodiment, the method may also include the steps of determining whether the mobile object is stationary, and establishing an azimuth reference value by sampling and saving a current azimuth value. In this regard, the method may also include the steps of establishing an azimuth reference error in the Kalman filter error state which is same as the azimuth reference value, and adjusting an error state covariance matrix to accurately reflect the azimuth reference error.
The above noted advantages and features of the present invention will become more apparent from the following detailed description of the preferred embodiments of the present invention when viewed in conjunction with the accompanying drawing.